chore(dgx): snapshot consolidation WIP pour transfert poc DGX
Regroupe le WIP non committé requis pour le clone/runtime DGX (Option A) : - api_stream.py : préflight replay + smoke santé modèles + handler 403 WP-B - de-hardcode VLM : vlm_config, gpu/*, vram_orchestrator, ollama_manager - stream_processor, semantic_matcher, agent_chat (app/planner/intent) - workflows.db (acquis ; le transfert artifacts le mettra à jour + rewrite chemins) - docs : plans DGX, benchmarks VLM/grounders, recherche SOTA, coordination 8 juin Snapshot destiné à la branche poc-dgx poussée sur Gitea pour cloner le DGX. Scan anti-secret : clean. graphify (repo embarqué) exclu. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Dom
@@ -31,7 +31,12 @@ from .replay_failure_logger import log_replay_failure
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from .replay_verifier import ReplayVerifier, VerificationResult
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from .replay_learner import ReplayLearner
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from .audit_trail import AuditTrail, AuditEntry
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from .agent_registry import AgentRegistry, AgentAlreadyEnrolledError, AgentRevokedError
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from .agent_registry import (
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AgentRegistry,
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AgentAlreadyEnrolledError,
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AgentRevokedError,
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FleetEnrollLockedError,
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)
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from .stream_processor import StreamProcessor, build_replay_from_raw_events, enrich_click_from_screenshot
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from .worker_stream import StreamWorker
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from .monitor_router import resolve_target_monitor # QW1 — résolution écran cible
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@@ -1595,6 +1600,20 @@ async def startup():
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logger.info("VLM model: %s", _vlm_model_name)
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print(f"\n VLM model: {_vlm_model_name}")
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# Smoke-test santé des modèles VLM/grounding (NON bloquant, thread daemon) :
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# détecte les modèles « aveugles » (sans capacité vision) au démarrage plutôt qu'en
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# échec silencieux runtime (incident 2026-06-08, UI-TARS réimporté sans mmproj → 500 masqué).
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def _smoke_model_health():
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try:
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from core.detection.model_health import smoke_check_models
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from core.detection import vlm_config
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_models = [vlm_config.get_vlm_model()] + list(getattr(vlm_config, "FALLBACK_VLM_MODELS", []))
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smoke_check_models(sorted({m for m in _models if m}))
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except Exception as _e: # ne jamais bloquer le démarrage
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logger.debug("smoke santé modèles ignoré: %s", _e)
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threading.Thread(target=_smoke_model_health, name="model-health-smoke", daemon=True).start()
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# Afficher le token API au démarrage pour que l'utilisateur puisse configurer l'agent
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_token_source = "env RPA_API_TOKEN" if os.environ.get("RPA_API_TOKEN") else "auto-généré"
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logger.info(f"API Token ({_token_source}): {API_TOKEN}")
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@@ -1648,7 +1667,15 @@ async def startup():
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)
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def _load_existing_workflows():
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def _iter_workflow_json_files(wf_dir: Path):
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"""Iterate workflow JSON files root-first, including machine subdirectories."""
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return sorted(
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wf_dir.rglob("*.json"),
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key=lambda p: (len(p.relative_to(wf_dir).parts), str(p.relative_to(wf_dir))),
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)
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def _load_existing_workflows(clear: bool = False) -> int:
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"""Charger les workflows JSON existants dans processor._workflows.
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Supporte deux formats :
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@@ -1657,6 +1684,10 @@ def _load_existing_workflows():
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"""
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from core.models.workflow_graph import Workflow
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if clear:
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with processor._data_lock:
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processor._workflows.clear()
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workflow_dirs = [
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ROOT_DIR / "data" / "workflows",
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ROOT_DIR / "data" / "training" / "workflows",
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@@ -1667,7 +1698,7 @@ def _load_existing_workflows():
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for wf_dir in workflow_dirs:
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if not wf_dir.exists():
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continue
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for wf_file in wf_dir.glob("*.json"):
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for wf_file in _iter_workflow_json_files(wf_dir):
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try:
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wf = Workflow.load_from_file(str(wf_file))
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if wf and hasattr(wf, 'workflow_id'):
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@@ -1689,7 +1720,10 @@ def _load_existing_workflows():
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except Exception as e:
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logger.debug(f"Skip workflow {wf_file.name}: {e}")
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logger.info(f"Workflows chargés depuis disque: {loaded}")
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with processor._data_lock:
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total = len(processor._workflows)
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logger.info(f"Workflows chargés depuis disque: {loaded} fichier(s), {total} en mémoire")
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return total
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@app.on_event("shutdown")
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@@ -2858,7 +2892,7 @@ async def reload_workflows():
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Appelé par le VWB après un export-for-lea pour que le streaming server
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voie immédiatement les nouveaux workflows sans redémarrage.
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"""
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count = processor.reload_workflows()
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count = _load_existing_workflows(clear=True)
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return {"success": True, "workflows_count": count}
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@@ -2901,6 +2935,129 @@ async def get_session(session_id: str):
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# =========================================================================
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# Marqueurs de dialogues/popups connus, détectables statiquement dans un workflow.
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_DIALOG_MARKERS = (
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"enregistrer sous",
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"confirmer l'enregistrement",
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"overwrite",
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"remplacer",
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"unsaved",
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"modifications non enregistrées",
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"save as",
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)
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def _iter_workflow_nodes(workflow: Any):
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"""Itère les nodes d'un workflow (objet Workflow OU dict), de façon tolérante."""
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if isinstance(workflow, dict):
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yield from workflow.get("nodes", [])
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return
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nodes = getattr(workflow, "nodes", None)
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if nodes is None:
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return
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# nodes peut être un dict {id: node} ou une liste
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yield from (nodes.values() if isinstance(nodes, dict) else nodes)
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def _node_text_blob(node: Any) -> str:
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"""Concatène les champs texte pertinents d'un node pour la détection de dialogue."""
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parts: List[str] = []
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if isinstance(node, dict):
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parts.append(str(node.get("label", "")))
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tmpl = node.get("template", {}) or {}
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window = tmpl.get("window", {}) if isinstance(tmpl, dict) else {}
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if isinstance(window, dict):
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parts.append(str(window.get("title_contains", "")))
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parts.append(str(window.get("title_pattern", "")))
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parts.append(str(node.get("expected_window_title", "")))
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else:
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parts.append(str(getattr(node, "label", "")))
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tmpl = getattr(node, "template", None)
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window = getattr(tmpl, "window", None) if tmpl is not None else None
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if window is not None:
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parts.append(str(getattr(window, "title_contains", "") or ""))
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return " ".join(p for p in parts if p).lower()
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def _detect_dialogs_static(workflow: Any) -> List[str]:
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"""Détecte statiquement les dialogues/popups attendus d'un workflow.
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Analyse les nodes (titres de fenêtre, labels) sans aucune exécution ni session.
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Retourne la liste dédupliquée des marqueurs de dialogue trouvés.
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"""
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found: List[str] = []
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for node in _iter_workflow_nodes(workflow):
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blob = _node_text_blob(node)
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for marker in _DIALOG_MARKERS:
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if marker in blob and marker not in found:
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found.append(marker)
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return found
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def _sanitize_action(action: Dict[str, Any]) -> Dict[str, Any]:
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"""Réduit une action à des champs non sensibles pour l'aperçu préflight."""
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return {
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"type": action.get("type") or action.get("action"),
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"target": (str(action.get("by_text") or action.get("target_spec") or "")[:60]) or None,
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"has_coords": action.get("x_pct") is not None,
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}
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def _build_preflight_report(
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workflow: Any, workflow_id: str, actions: List[Dict[str, Any]]
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) -> Dict[str, Any]:
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"""Construit le rapport de préflight (analyse pure, AUCUN effet de bord).
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Ne touche NI `_replay_queues`, NI `_replay_states`, NI aucun lock.
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"""
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from collections import Counter
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action_types = dict(Counter(
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(a.get("type") or a.get("action") or "unknown") for a in actions
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))
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name = workflow.get("name") if isinstance(workflow, dict) else getattr(workflow, "name", "")
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return {
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"workflow_known": True,
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"workflow_id": workflow_id,
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"workflow_name": name or "",
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"n_actions": len(actions),
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"action_types": action_types,
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"dialogs_detected": _detect_dialogs_static(workflow),
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"sample_actions": [_sanitize_action(a) for a in actions[:3]],
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"non_destructive": True,
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}
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class PreflightRequest(BaseModel):
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"""Requête de préflight replay (inspection non destructive d'un workflow)."""
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workflow_id: str
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params: Optional[Dict[str, Any]] = None
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@app.post("/api/v1/traces/stream/replay/preflight")
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async def preflight_replay(request: PreflightRequest):
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"""Préflight non destructif d'un workflow de replay.
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Prouve `commande → workflow connu → actions non vides → dialogues détectables`
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SANS injecter d'action, sans modifier `_replay_queues`/`_replay_states`, sans lock.
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"""
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workflow_id = request.workflow_id
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params = request.params or {}
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with processor._data_lock:
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workflow = processor._workflows.get(workflow_id)
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if not workflow:
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raise HTTPException(
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status_code=404,
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detail=f"Workflow '{workflow_id}' non trouvé. "
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f"Workflows disponibles : {list(processor._workflows.keys())[:20]}"
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)
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# Conversion en actions (fonction pure, sans effet de bord sur les queues)
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actions = _workflow_to_actions(workflow, params)
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return _build_preflight_report(workflow, workflow_id, actions)
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@app.post("/api/v1/traces/stream/replay")
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@@ -6859,6 +7016,18 @@ async def agents_enroll(request: AgentEnrollRequest):
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"existing": existing,
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},
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)
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except FleetEnrollLockedError:
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logger.warning(
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f"[FLEET] Enrolement refuse machine_id={machine_id} : parc verrouille "
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f"(RPA_FLEET_ENROLL_LOCKED)"
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)
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raise HTTPException(
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status_code=403,
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detail={
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"error": "fleet_enroll_locked",
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"message": "enrolement de nouveaux postes desactive (parc verrouille)",
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},
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)
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except ValueError as exc:
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raise HTTPException(status_code=400, detail=str(exc))
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@@ -2486,30 +2486,25 @@ class StreamProcessor:
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from core.models.workflow_graph import Workflow
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count = 0
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# Charger les workflows du dossier racine (rétrocompatibilité)
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for wf_file in sorted(workflows_dir.glob("*.json")):
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workflow_files = sorted(
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workflows_dir.rglob("*.json"),
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key=lambda p: (
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len(p.relative_to(workflows_dir).parts),
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str(p.relative_to(workflows_dir)),
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),
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)
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for wf_file in workflow_files:
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try:
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wf = Workflow.load_from_file(wf_file)
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rel_parts = wf_file.relative_to(workflows_dir).parts
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if len(rel_parts) > 1 and not hasattr(wf, '_machine_id'):
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wf._machine_id = rel_parts[0]
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self._workflows[wf.workflow_id] = wf
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count += 1
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except Exception as e:
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logger.warning(f"Impossible de charger {wf_file.name}: {e}")
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# Charger les workflows des sous-dossiers par machine
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for machine_dir in sorted(workflows_dir.iterdir()):
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if not machine_dir.is_dir():
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continue
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for wf_file in sorted(machine_dir.glob("*.json")):
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try:
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wf = Workflow.load_from_file(wf_file)
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# Stocker le machine_id dans les métadonnées du workflow
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if not hasattr(wf, '_machine_id'):
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wf._machine_id = machine_dir.name
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self._workflows[wf.workflow_id] = wf
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count += 1
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except Exception as e:
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logger.warning(f"Impossible de charger {wf_file.name}: {e}")
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if count:
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logger.info(f"{count} workflow(s) chargé(s) depuis {workflows_dir}")
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except ImportError:
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